Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Kanak Kalita; Janjhyam Venkata Naga Ramesh; Lenka Cepova; Sundaram B Pandya; Pradeep Jangir; Laith Abualigah

doi:10.1038/s41598-024-52083-7

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Sci Rep. 2024 Jan 20;14(1):1816. doi: 10.1038/s41598-024-52083-7.

Authors

Kanak Kalita^{1

2}, Janjhyam Venkata Naga Ramesh³, Lenka Cepova⁴, Sundaram B Pandya⁵, Pradeep Jangir⁶, Laith Abualigah^{7

8

9

10

11

12

13}

Affiliations

¹ Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India. drkanakkalita@veltech.edu.in.
² University Centre for Research and Development, Chandigarh University, Mohali, 140413, India. drkanakkalita@veltech.edu.in.
³ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522502, India.
⁴ Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800, Ostrava, Czech Republic.
⁵ Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch, 392 001, India.
⁶ Department of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602 105, India.
⁷ Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan.
⁸ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan.
⁹ MEU Research Unit, Middle East University, Amman, 11831, Jordan.
¹⁰ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon.
¹¹ School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia.
¹² School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia.
¹³ Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia.

Abstract

The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO .